Firecrawl vs TabbyML

Firecrawl (Web data API for AI) and TabbyML both appear on the Smoower Developer Ecosystem Index.

Firecrawl (rank #122) holds a modest lead over TabbyML (rank #569) on the overall Smoower ecosystem score (54 vs 35). The gap of 19 points reflects composite signals across code, docs, community and reach.

On code quality (the state of repositories, tests, releases and polish), Firecrawl is ahead of TabbyML. On education (docs, guides and learning material for developers), Firecrawl is clearly ahead of TabbyML. On community (issue response, PR reviews and discussion health), Firecrawl is slightly ahead of TabbyML. On reach (how visible the ecosystem is beyond its own repos), TabbyML is ahead of Firecrawl. On momentum (release cadence and how fast the ecosystem moves), Firecrawl is clearly ahead of TabbyML.

Firecrawl carries 199,181 GitHub stars across 101 public repos, with 28 repositories active in the last 90 days and 130 external contributors on record. TabbyML shows 34,010 stars across 18 public repos, 5 active in the last 90 days and 12 external contributors. The star gap on its own does not decide the comparison, but Firecrawl's footprint is roughly 5.9x larger, which usually shows up in downstream signals like inbound issues and third party integrations.

Firecrawl is the stronger read for anyone weighting education. TabbyML looks better where reach is the deciding factor. The table below breaks the scores down pillar by pillar; the linked profiles cover the underlying repos, docs and community signals in full.